class_docstr: Abstract object detector dataset module.
  
  This module implements some of the basic functionalities every object
  detector dataset usually needs.
  
  Object detection datasets are datasets that have ground-truth information
  consisting of rectangular bounding boxes.
  
  Attributes:
      dataset_dir (str): Base directory of the dataset.
      num_epochs (int): Number of epochs the dataset should iterate over.
      batch_size (int): Batch size the module should return.
      split (str): Split to consume the data from (usually "train", "val" or
          "test").
      image_min_size (int): Image minimum size, used for resizing images if
          needed.
      image_max_size (int): Image maximum size.
      random_shuffle (bool): To consume the dataset using random shuffle or
          to just use a regular FIFO queue.
docstr: Apply transformations to image and bboxes (if available).
  
  Transformations are applied according to the config values.
docstr_tok: ["Apply","transformations","to","image","and","bboxes","(","if","available",")",".","Transformations","are","applied","according","to","the","config","values","."]
code: def preprocess(self, image, bboxes=None):
      """Apply transformations to image and bboxes (if available).
  
      Transformations are applied according to the config values.
      """
      # Resize images (if needed)
      image, bboxes, applied_augmentations = self._augment(
          image, bboxes
      )
      image, bboxes, scale_factor = self._resize_image(image, bboxes)
  
      return (
          image,
          bboxes,
          {
              "scale_factor": scale_factor,
              "applied_augmentations": applied_augmentations,
          },
      )
  
file_path: luminoth/luminoth/datasets/object_detection_dataset.py
imports: import tensorflow
  from luminoth.datasets.base_dataset import BaseDataset
  from luminoth.utils.image import resize_image_fixed, resize_image, flip_image, random_patch, random_resize, random_distortion, expand
label_desc: Use image node_id 2 for calling the ObjectDetectionDataset obj's underlying member method code with example usage: obj.preprocess(image, bboxes) and returns: image,  bboxes,  dict